Convex Optimization

A successive centralized circumcentered-reflection method for the convex feasibility problem

  • Yunier Bello-Cruz
  • Roger Behling
  • A. N. Iusem
  • Luiz-Rafael Santos
  • Di Liu
    • Categories Convex Optimization Tags , , ,

    In this paper, we present a successive centralization process for the circumcentered-reflection scheme with several control sequences for solving the convex feasibility problem in Euclidean space. Assuming that a standard error bound holds, we prove the linear convergence of the method with the most violated constraint control sequence. Moreover, under additional smoothness assumptions on the … Read more

    Projection free methods on product domains

  • Immanuel M. Bomze
  • Francesco Rinaldi
  • Damiano Zeffiro
    • Categories Convex Optimization, Nonlinear Optimization Tags , ,

    Projection-free block-coordinate methods avoid high computational cost per iteration and at the same time exploit the particular problem structure of product domains. Frank-Wolfe-like approaches rank among the most popular ones of this type. However, as observed in the literature, there was a gap between the classical Frank-Wolfe theory and the block-coordinate case. Moreover, most of … Read more

    Semi-Infinite Generalized Disjunctive and Mixed Integer Convex Programs with(out) Uncertainty

  • Manish Bansal
    • Categories 0-1 Programming, Convex Optimization, Semi-infinite Programming

    In this paper, we introduce semi-infinite generalized disjunctive programs that are defined by logical propositions along with disjunctions of sets of logical equations and infinite number of algebraic inequalities. We denote these programs by SIGDPs. For SIGDPs with linear and convex inequalities, we present new reformulations: semi-infinite mixed-binary/disjunctive linear programs and semi-infinite mixed-binary/disjunctive convex programs, … Read more

    A Novel Stepsize for Gradient Descent Method

  • Pham Thi Hoai
    • Categories Constrained Nonlinear Optimization, Convex Optimization, Nonlinear Optimization Tags , , , ,

    In this paper, we propose a novel stepsize for the classical gradient descent scheme to solve unconstrained nonlinear optimization problems. We are concerned with the convex and smooth objective without the globally Lipschitz gradient condition. Our new method just needs the locally Lipschitz gradient but still gets the rate $O(\frac{1}{k})$ of $f(x^k)-f_*$ at most. By … Read more

    The Jordan algebraic structure of the rotated quadratic cone

  • Baha Alzalg
  • Karima Tamsaouete
    • Categories Convex and Nonsmooth Optimization, Convex Optimization, Quadratic Programming

    In this paper, we look into the rotated quadratic cone and analyze its algebraic structure. We construct an algebra associated with this cone and show that this algebra is a Euclidean Jordan algebra (EJA) with a certain inner product. We also demonstrate some spectral and algebraic characteristics of this EJA. The rotated quadratic cone is … Read more

    Superiorization: The asymmetric roles of feasibility-seeking and objective function reduction

  • Yair Censor
    • Categories Biomedical Applications, Constrained Nonlinear Optimization, Convex Optimization Tags , , , , , , , , , ,

    The superiorization methodology can be thought of as lying conceptually between feasibility-seeking and constrained minimization. It is not trying to solve the full-fledged constrained minimization problem composed from the modeling constraints and the chosen objective function. Rather, the task is to find a feasible point which is “superior” (in a well-defined manner) with respect to … Read more

    Orbital Crossover

  • Ethan Deakins
  • Bernard Knueven
  • James Ostrowski
    • Categories Convex Optimization, Linear Programming, Optimization Software Benchmark Tags , ,

    Symmetry in optimization has been known to wreak havoc in optimization algorithms. Often, some of the hardest instances are highly symmetric. This is not the case in linear programming, as symmetry allows one to reduce the size of the problem, possibly dramatically, while still maintaining the same optimal objective value. This is done by aggregating … Read more

    Regularized Nonsmooth Newton Algorithms for Best Approximation

  • Yair Censor
  • Walaa M. Moursi
  • Tyler Weames
  • Henry Wolkowicz
    • Categories Convex Optimization, Linear Programming, Nonsmooth Optimization Tags , , , , ,

    We consider the problem of finding the best approximation point from a polyhedral set, and its applications, in particular to solving large-scale linear programs. The classical projection problem has many various and many applications. We study a regularized nonsmooth Newton type solution method where the Jacobian is singular; and we compare the computational performance to … Read more

    Expected Value of Matrix Quadratic Forms with Wishart distributed Random Matrices

  • Melinda Hagedorn
    • Categories Convex Optimization, Data Science Theory, Stochastic Approaches Tags , , , , ,

    To explore the limits of a stochastic gradient method, it may be useful to consider an example consisting of an infinite number of quadratic functions. In this context, it is appropriate to determine the expected value and the covariance matrix of the stochastic noise, i.e. the difference of the true gradient and the approximated gradient … Read more

    Approximation hierarchies for copositive cone over symmetric cone and their comparison

  • Mitsuhiro Nishijima
  • Kazuhide Nakata
    • Categories Cone Programming, Convex Optimization, Global Optimization Theory Tags , , , ,

    We first provide an inner-approximation hierarchy described by a sum-of-squares (SOS) constraint for the copositive (COP) cone over a general symmetric cone. The hierarchy is a generalization of that proposed by Parrilo (2000) for the usual COP cone (over a nonnegative orthant). We also discuss its dual. Second, we characterize the COP cone over a … Read more